Demo Data from SIGIR 2026 / reviewers in the wild / expert
Makiko Saito
dblp:70/3746
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2001
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Graphics, computer vision, multimedia, augmented reality and games · 1Human-computer interaction and ubiquitous computing · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Human-computer interaction and pervasive computing
1 paper |
Interaction techniques and input · 56% Wearable and physiological sensing · 44% |
Topics — the 3 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Interaction techniques and input › input sensing
gesture recognition |
0.0 | 1 | 2001 | Real-Time Input of 3D Pose and Gestures of a User's Hand and Its Applications for HCI · VR 2001 |
Wearable and physiological sensing › motion capture
hand pose tracking |
0.0 | 1 | 2001 | Real-Time Input of 3D Pose and Gestures of a User's Hand and Its Applications for HCI · VR 2001 |
Interaction techniques and input › spatial interaction
3d interaction |
0.0 | 1 | 2001 | Real-Time Input of 3D Pose and Gestures of a User's Hand and Its Applications for HCI · VR 2001 |
Methods — techniques the papers use, named apart from their topics
neural network · 0.0multi-camera tracking · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2001 | Real-Time Input of 3D Pose and Gestures of a User's Hand and Its Applications for HCIabstractIntroduces a method for tracking a user's hand in 3D and recognizing the hand's gesture in real time without the use of any invasive devices attached to the hand. Our method uses multiple cameras for determining the position and orientation of a user's hand moving freely in a 3D space. In addition, the method identifies pre-determined gestures in a fast and robust manner by using a neural network which has been properly trained beforehand. This paper also describes results of user study of our proposed method and several types of applications, including 3D object handling for a desktop system and a 3D walkthrough for a large immersive display system. Yoichi Sato 0001, Makiko Saito, Hideki Koike |
VR | 2 |